precompile

Project.toml

@@ -8,6 +8,7 @@ CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
 CPUSummary = "2a0fbf3d-bb9c-48f3-b0a9-814d99fd7ab9"
 PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
+SnoopPrecompile = "66db9d55-30c0-4569-8b51-7e840670fc0c"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
@@ -18,6 +19,7 @@ CategoricalArrays = "0.9, 0.10"
 CPUSummary = "0.1"
 PrettyTables = "2"
 Requires = "1"
+SnoopPrecompile = "1"
 StatsBase = "0.29, 0.30, 0.31, 0.32, 0.33"
 StatsModels = "0.6, 0.7"
 Tables = "1"

src/MetidaBase.jl

@@ -4,8 +4,6 @@ module MetidaBase
 
     using Tables, PrettyTables, StatsModels, CategoricalArrays, Requires#, Reexport
 
-    #@reexport using StatsModels
-    #import DataFrames, TypedTables
     import StatsBase
     import StatsModels: StatisticalModel, RegressionModel
     import Tables: istable, columnaccess, columns, getcolumn, columnnames, schema, rowaccess, rows
@@ -19,6 +17,7 @@ module MetidaBase
     include("types.jl")
     include("utils.jl")
     include("iterators.jl")
+    include("precompile.jl")
 
     function __init__()
         @require DataFrames="a93c6f00-e57d-5684-b7b6-d8193f3e46c0" include("dataframes.jl")

src/precompile.jl

@@ -0,0 +1,46 @@
+import SnoopPrecompile
+
+SnoopPrecompile.@precompile_all_calls begin
+    sdfromcv(0.4)
+    varfromcv(0.4)
+    cvfromvar(0.4)
+    cvfromsd(0.4)
+    nonunique([1,2,3,3,4,5,6,6])
+    sortbyvec!([1,2,3,4,5,6,7,8], [2,5,3,1,8,4,6,7])
+    mt = metida_table([1,2,3], ["a", "b", "c"])
+    Tables.istable(mt) 
+    Tables.rowaccess(mt) 
+    mt = mt[:, [:x1, :x2]]
+    mtr = mt[1, :]
+    names(mt)
+    mt = MetidaBase.metida_table([1,2,3], ["a", "b", "c"], names = (:a, :b))
+    pushfirst!(mt, [0, " "])
+    ntr = NamedTuple{(:b, :a)}(["d", 10])
+    pushfirst!(mt, ntr)
+    size(mt, 1) == 5
+    Tables.rows(mt)
+    for (i,j) in enumerate(mt)
+        mt[i, :a] 
+    end
+    length(mt)
+    mt2 = MetidaBase.metida_table([1,2,3], ["a", "b", "c"], names = (:a, :b))
+    append!(mt, mt2)
+    mtd = MetidaBase.indsdict!(Dict(), mt)
+
+    v1 = [1, 2, -6, missing, NaN, 0]
+    itr1 = skipnanormissing(v1)
+    for i in itr1
+        !isnanormissing(i)
+    end
+    collect(itr1)
+    collect(eachindex(itr1)) 
+    eltype(itr1) 
+    itr2 = skipnonpositive(v1)
+    for i in itr2
+        ispositive(i)
+    end
+    collect(eachindex(itr2))
+    eltype(itr2)
+    collect(keys(itr2))
+    length(itr2)
+end

src/utils.jl

@@ -1,4 +1,5 @@
 # Заполняет словарь d индексами индивидуальных значений
+
 function indsdict!(d::Dict, cdata::Union{Tuple, NamedTuple, AbstractVector{AbstractVector}})
     @inbounds for (i, element) in enumerate(zip(cdata...))
         ind = ht_keyindex(d, element)
@@ -71,7 +72,6 @@ ispositive(x) = x > zero(x)
 
 # STATISTICS
 
-
 #CV2se
 """
     sdfromcv(cv::Real)::AbstractFloat

test/runtests.jl

@@ -2,19 +2,18 @@ using MetidaBase
 using Test, Tables, TypedTables, DataFrames, CSV
 
 
-struct ExampleIDStruct <: MetidaBase.AbstractSubject
-    #time
-    #obs
-    id::Dict
- end
-struct ExampleResultStruct{T} <: MetidaBase.AbstractSubjectResult{T}
-    data::T
-    result::Dict
-end
-
-
 @testset "MetidaBase.jl" begin
 
+    struct ExampleIDStruct <: MetidaBase.AbstractSubject
+        #time
+        #obs
+        id::Dict
+     end
+    struct ExampleResultStruct{T} <: MetidaBase.AbstractSubjectResult{T}
+        data::T
+        result::Dict
+    end
+
     io       = IOBuffer();
 #####################################################################
 # metida_table